Samuel Gershman
Samuel Gershman
Associate Professor, Harvard University
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Building machines that learn and think like people
BM Lake, TD Ullman, JB Tenenbaum, SJ Gershman
Behavioral and brain sciences 40, 2017
Model-based influences on humans' choices and striatal prediction errors
ND Daw, SJ Gershman, B Seymour, P Dayan, RJ Dolan
Neuron 69 (6), 1204-1215, 2011
A tutorial on Bayesian nonparametric models
SJ Gershman, DM Blei
Journal of Mathematical Psychology 56, 1-12, 2012
Computational rationality: A converging paradigm for intelligence in brains, minds, and machines
SJ Gershman, EJ Horvitz, JB Tenenbaum
Science 349 (6245), 273-278, 2015
The curse of planning: Dissecting multiple reinforcement learning systems by taxing the central executive
AR Otto, SJ Gershman, AB Markman, ND Daw
Psychological Science 24 (5), 751-761, 2013
The hippocampus as a predictive map
KL Stachenfeld, MM Botvinick, SJ Gershman
Nature Neuroscience 20, 1643-1653, 2017
Context, learning, and extinction
SJ Gershman, DM Blei, Y Niv
Psychological Review 117 (1), 197-209, 2010
Reinforcement learning in multidimensional environments relies on attention mechanisms
Y Niv, R Daniel, A Geana, SJ Gershman, YC Leong, A Radulescu, ...
Journal of Neuroscience 35 (21), 8145-8157, 2015
Reinforcement learning and episodic memory in humans and animals: an integrative framework
SJ Gershman, ND Daw
Annual review of psychology 68, 101-128, 2017
Learning latent structure: carving nature at its joints
SJ Gershman, Y Niv
Current Opinion in Neurobiology 20 (2), 251-256, 2010
Amortized Inference in Probabilistic Reasoning
SJ Gershman, ND Goodman
Proceedings of the 36th Annual Cognitive Science Society, 2013
Retrospective revaluation in sequential decision making: A tale of two systems
SJ Gershman, AB Markman, AR Otto
Journal of Experimental Psychology: General 143, 182-194, 2014
The successor representation in human reinforcement learning
I Momennejad, EM Russek, JH Cheong, MM Botvinick, ND Daw, ...
Nature Human Behaviour 1 (9), 680-692, 2017
Nonparametric variational inference
S Gershman, M Hoffman, D Blei
Proceedings of the 29th International Conference on Machine Learning, 2012
Predictive representations can link model-based reinforcement learning to model-free mechanisms
EM Russek, I Momennejad, MM Botvinick, SJ Gershman, ND Daw
PLoS computational biology 13 (9), e1005768, 2017
Deep successor reinforcement learning
TD Kulkarni, A Saeedi, S Gautam, SJ Gershman
arXiv preprint arXiv:1606.02396, 2016
Human reinforcement learning subdivides structured action spaces by learning effector-specific values
SJ Gershman, B Pesaran, ND Daw
Journal of Neuroscience 29 (43), 13524-13531, 2009
Interplay of approximate planning strategies
QJM Huys, N Lally, P Faulkner, N Eshel, E Seifritz, SJ Gershman, ...
Proceedings of the National Academy of Sciences 112 (10), 3098-3103, 2015
Multistability and perceptual inference
SJ Gershman, E Vul, JB Tenenbaum
Neural Computation 24, 1-24, 2012
Human memory reconsolidation can be explained using the temporal context model
PB Sederberg, SJ Gershman, SM Polyn, KA Norman
Psychonomic Bulletin & Review 18, 1-14, 2011
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